(734) 615-1915

Applications:
Complex Systems, Computer Science, Economics, Finance and Business, Engineering
Methodologies:
Bayesian Methods, Causal Inference, Computing, Data Mining, Information Theory, Machine Learning, Mathematical and Statistical Modeling, Networks, Optimization, Statistics
Relevant Projects:

NSF


Vijay Subramanian

Associate Professor

EECS, College of Engineering

Associate Professor of Electrical Engineering and Computer Science, College of Engineering

Professor Subramanian is interested in a variety of stochastic modeling, decision and control theoretic, and applied probability questions concerned with networks. Examples include analysis of random graphs, analysis of processes like cascades on random graphs, network economics, analysis of e-commerce systems, mean-field games, network games, telecommunication networks, load-balancing in large server farms, and information assimilation, aggregation and flow in networks especially with strategic users.